An important challenge in physical HRI is the ability of robots to infer human co-worker's goals and states. To address this challenge, several recent works have proposed inference techniques and applications. The study in [1]} showed that robot adaptation to the human task execution can improve task-related performance. Robot adaption to the human state can also improve ergonomics [2]}. Finally, user satisfaction can also be improved by robot adaptation [3]}. Nevertheless, to adapt to these different aspects, the robot should be able to infer the human state, which often requires complex sensory setups [4]}, [5]}. However, complex measurement systems are not always available or feasible for practical applications, thus some states can be modeled, such as the desired motion of the robot/payload [6]}, [7]}, [8]}, [9]}, human actions [10]} and preferences [11]}, [12]}, human physical fatigue [2]}, cognitive stress [14]}, and suitability of shared workspace [15]}, [16]}, [17]}, [18]}.
